Intelligent mechatronic systems, such as self-optimizing systems, allow an adaptation of the system behavior at runtime based on the current situation. To do so, they generally select among several pre-defined working points. A common method to determine working points for a mechatronic system is to use model-based multiobjective optimization. It allows finding compromises among conflicting objectives, called objective functions, by adapting parameters. To evaluate the system behavior for different parameter sets, a model of the system behavior is included in the objective functions and is evaluated during each function call. Intelligent mechatronic systems also have the ability to adapt their behavior based on their current reliability, thus increasing their availability, or on changed safety requirements; all of which are summed up by the common term dependability. To allow this adaptation, dependability can be considered in multiobjective optimization by including dependability-related objective functions. However, whereas performance-related objective functions are easily found, formulation of dependability-related objective functions is highly system-specific and not intuitive, making it complex and error-prone. Since each mechatronic system is different, individual failure modes have to be taken into account, which need to be found using common methods such as Failure-Modes and Effects Analysis or Fault Tree Analysis. Using component degradation models, which again are specific to the system at hand, the main loading factors can be determined. By including these in the model of the system behavior, the relation between working point and dependability can be formulated as an objective function. In our work, this approach is presented in more detail. It is exemplified using an actively actuated single plate dry clutch system. Results show that this approach is suitable for formulating dependability-related objective functions and that these can be used to extend system lifetime by adapting system behavior.

@ARTICLE{Meyer2014b,
howpublished = {Journal},
author = {Meyer, Tobias AND Sondermann-W{\"o}lke, Christoph AND Sextro, Walter},
title = {Method to Identify Dependability Objectives in Multiobjective Optimization
Problem},
journal = {Procedia Technology},
year = {2014},
volume = {15},
pages = {46-53},
abstract = {Intelligent mechatronic systems, such as self-optimizing systems,
allow an adaptation of the system behavior at runtime based on the
current situation. To do so, they generally select among several
pre-defined working points. A common method to determine working
points for a mechatronic system is to use model-based multiobjective
optimization. It allows finding compromises among conflicting objectives,
called objective functions, by adapting parameters. To evaluate the
system behavior for different parameter sets, a model of the system
behavior is included in the objective functions and is evaluated
during each function call. Intelligent mechatronic systems also have
the ability to adapt their behavior based on their current reliability,
thus increasing their availability, or on changed safety requirements;
all of which are summed up by the common term dependability. To allow
this adaptation, dependability can be considered in multiobjective
optimization by including dependability-related objective functions.
However, whereas performance-related objective functions are easily
found, formulation of dependability-related objective functions is
highly system-specific and not intuitive, making it complex and error-prone.
Since each mechatronic system is different, individual failure modes
have to be taken into account, which need to be found using common
methods such as Failure-Modes and Effects Analysis or Fault Tree
Analysis. Using component degradation models, which again are specific
to the system at hand, the main loading factors can be determined.
By including these in the model of the system behavior, the relation
between working point and dependability can be formulated as an objective
function. In our work, this approach is presented in more detail.
It is exemplified using an actively actuated single plate dry clutch
system. Results show that this approach is suitable for formulating
dependability-related objective functions and that these can be used
to extend system lifetime by adapting system behavior.},
booktitle = {Conference Proceedings of the 2nd International Conference on System-Integrated
Intelligence},
doi = {10.1016/j.protcy.2014.09.033},
keywords = {Self-optimization; multiobjective optimization; objective function;
dependability; intelligent system; behavior adaptation},
url = {http://www.sciencedirect.com/science/article/pii/S2212017314001480}
}